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Super-Resolution by Predicting Offsets: An Ultra-Efficient Super-Resolution Network for Rasterized Images

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Computer Vision – ECCV 2022 (ECCV 2022)

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Rendering high-resolution (HR) graphics brings substantial computational costs. Efficient graphics super-resolution (SR) methods may achieve HR rendering with small computing resources and have attracted extensive research interests in industry and research communities. We present a new method for real-time SR for computer graphics, namely Super-Resolution by Predicting Offsets (SRPO). Our algorithm divides the image into two parts for processing, i.e., sharp edges and flatter areas. For edges, different from the previous SR methods that take the anti-aliased images as inputs, our proposed SRPO takes advantage of the characteristics of rasterized images to conduct SR on the rasterized images. To complement the residual between HR and low-resolution (LR) rasterized images, we train an ultra-efficient network to predict the offset maps to move the appropriate surrounding pixels to the new positions. For flat areas, we found simple interpolation methods can already generate reasonable output. We finally use a guided fusion operation to integrate the sharp edges generated by the network and flat areas by the interpolation method to get the final SR image. The proposed network only contains 8,434 parameters and can be accelerated by network quantization. Extensive experiments show that the proposed SRPO can achieve superior visual effects at a smaller computational cost than the existing state-of-the-art methods.

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  1. 1.

    For FSRCNN, we use the hyper-parameter of \(D=56\), \(S=12\) and \(M=4\).

  2. 2.

    For ESPCN, we use the hyper-parameter of \(D=22\) and \(S=32\).

  3. 3.

    For eSR family, we select four versions of eSR: eSR-MAX with \(K=3\) and \(C=1\); eSR-TR with \(K=5\) and \(C=2\); eSR-TM with \(K=7\) and \(C=4\); eSR-CNN with \(C=4\), \(D=3\) and \(S=6\).


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This work was partly supported by SZSTC Grant No. JCYJ20190809172201639 and WDZC20200820200655001, Shenzhen Key Laboratory ZDSYS20210623092001004. We sincerely thank Yongfei Pu, Yuanlong Li, Jieming Li and Yuanlin Chen for contributing to this study.

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Correspondence to Jinjin Gu or Chun Yuan .

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Gu, J. et al. (2022). Super-Resolution by Predicting Offsets: An Ultra-Efficient Super-Resolution Network for Rasterized Images. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13679. Springer, Cham.

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